{
 "cells": [
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a09b627",
    "execution_start": 1627770706236,
    "execution_millis": 3256,
    "cell_id": "00000-0e695db4-9ad4-4f9a-ace1-93b81fa4d428",
    "deepnote_cell_type": "code"
   },
   "source": "!! pip install pingouin",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 1,
     "data": {
      "text/plain": "['Requirement already satisfied: pingouin in /root/venv/lib/python3.7/site-packages (0.3.12)',\n 'Requirement already satisfied: outdated in /root/venv/lib/python3.7/site-packages (from pingouin) (0.2.1)',\n 'Requirement already satisfied: pandas-flavor>=0.1.2 in /root/venv/lib/python3.7/site-packages (from pingouin) (0.2.0)',\n 'Requirement already satisfied: scipy>=1.3 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (1.7.0)',\n 'Requirement already satisfied: pandas>=0.24 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (1.2.5)',\n 'Requirement already satisfied: scikit-learn in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (0.24.2)',\n 'Requirement already satisfied: seaborn>=0.9.0 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (0.11.1)',\n 'Requirement already satisfied: statsmodels>=0.10.0 in /root/venv/lib/python3.7/site-packages (from pingouin) (0.12.2)',\n 'Requirement already satisfied: numpy>=1.15 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (1.19.5)',\n 'Requirement already satisfied: tabulate in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (0.8.9)',\n 'Requirement already satisfied: matplotlib>=3.0.2 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (3.4.2)',\n 'Requirement already satisfied: pillow>=6.2.0 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from matplotlib>=3.0.2->pingouin) (8.3.1)',\n 'Requirement already satisfied: cycler>=0.10 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from matplotlib>=3.0.2->pingouin) (0.10.0)',\n 'Requirement already satisfied: python-dateutil>=2.7 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from matplotlib>=3.0.2->pingouin) (2.8.2)',\n 'Requirement already satisfied: kiwisolver>=1.0.1 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from matplotlib>=3.0.2->pingouin) (1.3.1)',\n 'Requirement already satisfied: pyparsing>=2.2.1 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from matplotlib>=3.0.2->pingouin) (2.4.7)',\n 'Requirement already satisfied: six in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from cycler>=0.10->matplotlib>=3.0.2->pingouin) (1.16.0)',\n 'Requirement already satisfied: pytz>=2017.3 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pandas>=0.24->pingouin) (2021.1)',\n 'Requirement already satisfied: xarray in /root/venv/lib/python3.7/site-packages (from pandas-flavor>=0.1.2->pingouin) (0.19.0)',\n 'Requirement already satisfied: patsy>=0.5 in /root/venv/lib/python3.7/site-packages (from statsmodels>=0.10.0->pingouin) (0.5.1)',\n 'Requirement already satisfied: littleutils in /root/venv/lib/python3.7/site-packages (from outdated->pingouin) (0.2.2)',\n 'Requirement already satisfied: requests in /shared-libs/python3.7/py/lib/python3.7/site-packages (from outdated->pingouin) (2.26.0)',\n 'Requirement already satisfied: idna<4,>=2.5 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from requests->outdated->pingouin) (3.2)',\n 'Requirement already satisfied: charset-normalizer~=2.0.0 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from requests->outdated->pingouin) (2.0.3)',\n 'Requirement already satisfied: urllib3<1.27,>=1.21.1 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from requests->outdated->pingouin) (1.26.6)',\n 'Requirement already satisfied: certifi>=2017.4.17 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from requests->outdated->pingouin) (2021.5.30)',\n 'Requirement already satisfied: threadpoolctl>=2.0.0 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from scikit-learn->pingouin) (2.2.0)',\n 'Requirement already satisfied: joblib>=0.11 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from scikit-learn->pingouin) (1.0.1)',\n 'Requirement already satisfied: setuptools>=40.4 in /root/venv/lib/python3.7/site-packages (from xarray->pandas-flavor>=0.1.2->pingouin) (57.1.0)',\n 'WARNING: You are using pip version 21.1.3; however, version 21.2.2 is available.',\n \"You should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.\"]"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ded401e6",
    "execution_start": 1627770709500,
    "execution_millis": 1415,
    "cell_id": "00001-34dba06f-0edb-4b1d-9631-4b90c8e9c450",
    "deepnote_cell_type": "code"
   },
   "source": "import numpy as np\nimport pandas as pd\nimport scipy\nfrom scipy import stats\n\nimport pingouin as pg\n\nfrom numpy import mean\nfrom numpy import std\nfrom numpy.random import randn\nfrom numpy.random import seed\nfrom matplotlib import pyplot\n\n# seed random number generator\nseed(1)\n\n# read data from CSV\ndf = pd.read_csv(\"Data_ahmad.csv\")\ndf1 = df[(df['Group'] != \"Belgium\") & (df['Group'] != \"Norway\")]",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T17:35:22.724114Z",
     "start_time": "2021-03-18T17:35:21.632259Z"
    },
    "deepnote_to_be_reexecuted": false,
    "source_hash": "487e9808",
    "execution_start": 1627770710929,
    "execution_millis": 4,
    "cell_id": "00002-2019c901-6576-4818-a825-20686b5608b1",
    "deepnote_cell_type": "code"
   },
   "source": "# Common imports\nimport numpy as np # numpy is THE toolbox for scientific computing with python\nimport pandas as pd # pandas provides THE data structure and data analysis tools for data scientists \n\n# maximum number of columns\npd.set_option(\"display.max_rows\", 101)\npd.set_option(\"display.max_columns\", 101)\n\n# To plot pretty figures\n%matplotlib inline\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nmpl.rc('axes', labelsize=14)\nmpl.rc('xtick', labelsize=12)\nmpl.rc('ytick', labelsize=12)\n\nfrom warnings import filterwarnings\nfilterwarnings('ignore')",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": "df",
   "metadata": {
    "tags": [],
    "cell_id": "00003-8092db33-0927-4ec3-bfbe-573089f36b9c",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "f804c160",
    "execution_start": 1627770711026,
    "execution_millis": 39,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 4,
     "data": {
      "application/vnd.deepnote.dataframe.v2+json": {
       "row_count": 19,
       "column_count": 6,
       "columns": [
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         "name": "Group",
         "dtype": "object",
         "stats": {
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          "nan_count": 0,
          "categories": [
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            "count": 1
           },
           {
            "name": "Spain",
            "count": 1
           },
           {
            "name": "17 others",
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           }
          ]
         }
        },
        {
         "name": "vitd",
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        {
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        },
        {
         "name": "CMR_D3pos",
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       ],
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         "_deepnote_index_column": 0
        },
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         "_deepnote_index_column": 1
        },
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        },
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        },
        {
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         "CMR_UN": 145.43,
         "CMR_Rigid": 114.16,
         "CMR_D3pos": 155.46,
         "_deepnote_index_column": 4
        },
        {
         "Group": "Italy",
         "vitd": 20,
         "CMR": 140,
         "CMR_UN": 140,
         "CMR_Rigid": 140,
         "CMR_D3pos": 120,
         "_deepnote_index_column": 5
        },
        {
         "Group": "Germany",
         "vitd": 20.04,
         "CMR": 61.7,
         "CMR_UN": 59.69,
         "CMR_Rigid": 49.78,
         "CMR_D3pos": 51.56,
         "_deepnote_index_column": 6
        },
        {
         "Group": "Austria",
         "vitd": 22.4,
         "CMR": 81.4,
         "CMR_UN": 67.24,
         "CMR_Rigid": 58.68,
         "CMR_D3pos": 66.28,
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        {
         "Group": "Ireland",
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        },
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         "Group": "Greece",
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         "CMR_UN": 52.82,
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         "_deepnote_index_column": 9
        },
        {
         "Group": "Netherlands",
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         "CMR_UN": 63.11,
         "CMR_Rigid": 43.27,
         "CMR_D3pos": 59.25,
         "_deepnote_index_column": 10
        },
        {
         "Group": "France",
         "vitd": 24,
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         "CMR_Rigid": 94.42,
         "CMR_D3pos": 82.83,
         "_deepnote_index_column": 11
        },
        {
         "Group": "Hungary",
         "vitd": 24.24,
         "CMR": 122.3,
         "CMR_UN": 91.73,
         "CMR_Rigid": 75.36,
         "CMR_D3pos": 89.98,
         "_deepnote_index_column": 12
        },
        {
         "Group": "Czechia",
         "vitd": 25,
         "CMR": 141.3,
         "CMR_UN": 106.74,
         "CMR_Rigid": 108.16,
         "CMR_D3pos": 101.94,
         "_deepnote_index_column": 13
        },
        {
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         "vitd": 26,
         "CMR": 33.5,
         "CMR_UN": 26.95,
         "CMR_Rigid": 20.45,
         "CMR_D3pos": 23.93,
         "_deepnote_index_column": 14
        },
        {
         "Group": "Norway",
         "vitd": 26,
         "CMR": 10,
         "CMR_UN": 7.12,
         "CMR_Rigid": 5.59,
         "CMR_D3pos": 7.14,
         "_deepnote_index_column": 15
        },
        {
         "Group": "Finland",
         "vitd": 27.08,
         "CMR": 11.6,
         "CMR_UN": 10.21,
         "CMR_Rigid": 7.09,
         "CMR_D3pos": 8.04,
         "_deepnote_index_column": 16
        },
        {
         "Group": "Sweden",
         "vitd": 29.4,
         "CMR": 109,
         "CMR_UN": 91.23,
         "CMR_Rigid": 62.63,
         "CMR_D3pos": 71.63,
         "_deepnote_index_column": 17
        },
        {
         "Group": "Slovakia",
         "vitd": 32.6,
         "CMR": 71.3,
         "CMR_UN": 44.18,
         "CMR_Rigid": 60.09,
         "CMR_D3pos": 42.27,
         "_deepnote_index_column": 18
        }
       ],
       "rows_bottom": null
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      "text/plain": "          Group   vitd    CMR  CMR_UN  CMR_Rigid  CMR_D3pos\n0      Portugal  15.60   97.3   93.07      71.04      97.30\n1         Spain  17.00  118.6  104.42     108.57     110.13\n2   Switzerland  18.40  104.4   84.54      66.98      94.71\n3            UK  18.96  141.7  110.90     127.99     125.51\n4       Belgium  19.72  178.4  145.43     114.16     155.46\n5         Italy  20.00  140.0  140.00     140.00     120.00\n6       Germany  20.04   61.7   59.69      49.78      51.56\n7       Austria  22.40   81.4   67.24      58.68      66.28\n8       Ireland  22.56   58.1   33.47      44.18      45.65\n9        Greece  23.18   53.7   52.82      44.43      40.66\n10  Netherlands  23.80   79.0   63.11      43.27      59.25\n11       France  24.00  111.5   94.53      94.42      82.83\n12      Hungary  24.24  122.3   91.73      75.36      89.98\n13      Czechia  25.00  141.3  106.74     108.16     101.94\n14      Denmark  26.00   33.5   26.95      20.45      23.93\n15       Norway  26.00   10.0    7.12       5.59       7.14\n16      Finland  27.08   11.6   10.21       7.09       8.04\n17       Sweden  29.40  109.0   91.23      62.63      71.63\n18     Slovakia  32.60   71.3   44.18      60.09      42.27",
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      },
      "text/plain": "          Group   vitd    CMR  CMR_UN  CMR_Rigid  CMR_D3pos\n0      Portugal  15.60   97.3   93.07      71.04      97.30\n1         Spain  17.00  118.6  104.42     108.57     110.13\n2   Switzerland  18.40  104.4   84.54      66.98      94.71\n3            UK  18.96  141.7  110.90     127.99     125.51\n5         Italy  20.00  140.0  140.00     140.00     120.00\n6       Germany  20.04   61.7   59.69      49.78      51.56\n7       Austria  22.40   81.4   67.24      58.68      66.28\n8       Ireland  22.56   58.1   33.47      44.18      45.65\n9        Greece  23.18   53.7   52.82      44.43      40.66\n10  Netherlands  23.80   79.0   63.11      43.27      59.25\n11       France  24.00  111.5   94.53      94.42      82.83\n12      Hungary  24.24  122.3   91.73      75.36      89.98\n13      Czechia  25.00  141.3  106.74     108.16     101.94\n14      Denmark  26.00   33.5   26.95      20.45      23.93\n16      Finland  27.08   11.6   10.21       7.09       8.04\n17       Sweden  29.40  109.0   91.23      62.63      71.63\n18     Slovakia  32.60   71.3   44.18      60.09      42.27",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Group</th>\n      <th>vitd</th>\n      <th>CMR</th>\n      <th>CMR_UN</th>\n      <th>CMR_Rigid</th>\n      <th>CMR_D3pos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Portugal</td>\n      <td>15.60</td>\n      <td>97.3</td>\n      <td>93.07</td>\n      <td>71.04</td>\n      <td>97.30</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Spain</td>\n      <td>17.00</td>\n      <td>118.6</td>\n      <td>104.42</td>\n      <td>108.57</td>\n      <td>110.13</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Switzerland</td>\n      <td>18.40</td>\n      <td>104.4</td>\n      <td>84.54</td>\n      <td>66.98</td>\n      <td>94.71</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>UK</td>\n      <td>18.96</td>\n      <td>141.7</td>\n      <td>110.90</td>\n      <td>127.99</td>\n      <td>125.51</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Italy</td>\n      <td>20.00</td>\n      <td>140.0</td>\n      <td>140.00</td>\n      <td>140.00</td>\n      <td>120.00</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Germany</td>\n      <td>20.04</td>\n      <td>61.7</td>\n      <td>59.69</td>\n      <td>49.78</td>\n      <td>51.56</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Austria</td>\n      <td>22.40</td>\n      <td>81.4</td>\n      <td>67.24</td>\n      <td>58.68</td>\n      <td>66.28</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Ireland</td>\n      <td>22.56</td>\n      <td>58.1</td>\n      <td>33.47</td>\n      <td>44.18</td>\n      <td>45.65</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Greece</td>\n      <td>23.18</td>\n      <td>53.7</td>\n      <td>52.82</td>\n      <td>44.43</td>\n      <td>40.66</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Netherlands</td>\n      <td>23.80</td>\n      <td>79.0</td>\n      <td>63.11</td>\n      <td>43.27</td>\n      <td>59.25</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>France</td>\n      <td>24.00</td>\n      <td>111.5</td>\n      <td>94.53</td>\n      <td>94.42</td>\n      <td>82.83</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Hungary</td>\n      <td>24.24</td>\n      <td>122.3</td>\n      <td>91.73</td>\n      <td>75.36</td>\n      <td>89.98</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Czechia</td>\n      <td>25.00</td>\n      <td>141.3</td>\n      <td>106.74</td>\n      <td>108.16</td>\n      <td>101.94</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Denmark</td>\n      <td>26.00</td>\n      <td>33.5</td>\n      <td>26.95</td>\n      <td>20.45</td>\n      <td>23.93</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Finland</td>\n      <td>27.08</td>\n      <td>11.6</td>\n      <td>10.21</td>\n      <td>7.09</td>\n      <td>8.04</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Sweden</td>\n      <td>29.40</td>\n      <td>109.0</td>\n      <td>91.23</td>\n      <td>62.63</td>\n      <td>71.63</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Slovakia</td>\n      <td>32.60</td>\n      <td>71.3</td>\n      <td>44.18</td>\n      <td>60.09</td>\n      <td>42.27</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5f223377",
    "execution_start": 1627770711190,
    "execution_millis": 8,
    "cell_id": "00004-352a977d-468c-4d29-855b-6601c4bafe3c",
    "deepnote_cell_type": "code"
   },
   "source": "print(scipy.stats.spearmanr(df[\"vitd\"], df[\"CMR\"], axis=0, nan_policy='propagate'))\nprint(scipy.stats.spearmanr(df1[\"vitd\"], df1[\"CMR\"], axis=0, nan_policy='propagate'))\nprint(scipy.stats.spearmanr(df[\"vitd\"], df[\"CMR_UN\"], axis=0, nan_policy='propagate'))\nprint(scipy.stats.spearmanr(df[\"vitd\"], df[\"CMR_Rigid\"], axis=0, nan_policy='propagate'))\nprint(scipy.stats.spearmanr(df[\"vitd\"], df[\"CMR_D3pos\"], axis=0, nan_policy='propagate'))",
   "outputs": [
    {
     "name": "stdout",
     "text": "SpearmanrResult(correlation=-0.43001320506473206, pvalue=0.06611844456588518)\nSpearmanrResult(correlation=-0.30637254901960786, pvalue=0.23168960494057989)\nSpearmanrResult(correlation=-0.5511189648584729, pvalue=0.014456542544782423)\nSpearmanrResult(correlation=-0.5195261579557579, pvalue=0.022624312008638143)\nSpearmanrResult(correlation=-0.6564283212008561, pvalue=0.002268638568612114)\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e76172c0",
    "execution_start": 1627770711191,
    "execution_millis": 7,
    "cell_id": "00005-68205dab-2525-4904-83da-8065c4fd2628",
    "deepnote_cell_type": "code"
   },
   "source": "print(scipy.stats.pearsonr(df[\"vitd\"], df[\"CMR\"]))\nprint(scipy.stats.pearsonr(df1[\"vitd\"], df1[\"CMR\"]))\nprint(scipy.stats.pearsonr(df[\"vitd\"], df[\"CMR_UN\"]))\nprint(scipy.stats.pearsonr(df[\"vitd\"], df[\"CMR_Rigid\"]))\nprint(scipy.stats.pearsonr(df[\"vitd\"], df[\"CMR_D3pos\"]))",
   "outputs": [
    {
     "name": "stdout",
     "text": "(-0.41538466742992153, 0.07695713714733686)\n(-0.3471042175280985, 0.17224374060524889)\n(-0.5112794929618234, 0.025263499186841)\n(-0.46618114042599923, 0.04423768506574241)\n(-0.5997126124188954, 0.006645162880162892)\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ea676b26",
    "execution_start": 1627770711238,
    "execution_millis": 1168,
    "cell_id": "00008-283d4df5-7ad7-408a-9ba2-84b90db632c9",
    "deepnote_cell_type": "code"
   },
   "source": "import seaborn as sns\nfrom matplotlib.lines import Line2D\n\nsns.set_style('white')\nsns.set_context('paper', font_scale=2)\nsns.set_style('ticks')\nsns.set_theme(color_codes=True)\n#fig = plt.style.use('fivethirtyeight')\n\nfig, main_ax = plt.subplots()\nscat1 = sns.regplot(x = 'vitd', y = 'CMR', data = df, ci = 95, truncate=False, ax=main_ax)\nscat1 = sns.regplot(x = 'vitd', y = 'CMR', data = df1, ci = 95, truncate=False, ax=main_ax)\nscat2 = sns.regplot(x = 'vitd', y = 'CMR_UN', data = df, ci = 95, truncate=False, ax=main_ax)\nscat3 = sns.regplot(x = 'vitd', y = 'CMR_Rigid', data = df, ci = 95, truncate=False, ax=main_ax)\nscat4 = sns.regplot(x = 'vitd', y = 'CMR_D3pos', data = df, ci = 95, truncate=False, ax=main_ax)\n\nmain_ax.legend([scat1, scat2, scat3, scat4], [ \"CMR\", \"CMR (UN)\", \"CMR_Rigid\", \"CMR_D3pos\"])\n\nplt.xlabel(\"Vitamin D [ng/mL]\")\nplt.ylabel(\"Mortality coefficient\")\nplt.show(fig)",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
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